Managing chains of application functions over multi-technology edge networks
Files
Accepted manuscript
Date
2021-03
Authors
Akhtar, Nabeel
Matta, Ibrahim
Raza, Ali
Goratti, Leonardo
Braun, Torsten
Esposito, Flavio
Version
Accepted manuscript
OA Version
Citation
N. Akhtar, I. Matta, A. Raza, L. Goratti, T. Braun, F. Esposito. 2021. "Managing Chains of Application Functions Over Multi-Technology Edge Networks." IEEE Transactions on Network and Service Management, Volume 18, Issue 1, pp. 511 - 525. https://doi.org/10.1109/tnsm.2021.3050009
Abstract
Next-generation networks are expected to provide higher data rates and ultra-low latency in support of demanding applications, such as virtual and augmented reality, robots and drones, etc. To meet these stringent requirements of applications, edge computing constitutes a central piece of the solution architecture wherein functional components of an application can be deployed over the edge network to reduce bandwidth demand over the core network while providing ultra-low latency communication to users. In this article, we provide solutions to resource orchestration and management for applications over a virtualized client-edge-server infrastructure. We investigate the problem of optimal placement of pipelines of application functions (virtual service chains) and the steering of traffic through them, over a multi-technology edge network model consisting of both wired and wireless millimeter-wave (mmWave) links. This problem is NP-hard. We provide a comprehensive “microscopic” binary integer program to model the system, along with a heuristic that is one order of magnitude faster than optimally solving the problem. Extensive evaluations demonstrate the benefits of orchestrating virtual service chains (by distributing them over the edge network) compared to a baseline “middlebox” approach in terms of overall admissible virtual capacity. Moreover, we observe significant gains when deploying a small number of mmWave links that complement the Wire physical infrastructure in high node density networks.